Structural Design Recommendations in the Early Design Phase using Machine Learning
This addresses the challenge for architects and engineers of costly late-stage design changes by enabling real-time structural exploration during conceptual design, though it is incremental as it focuses on a specific domain of orthogonal, metal, rigid structures.
The paper tackles the problem of integrating structural engineering feedback into the architectural early design phase by introducing ApproxiFramer, a machine learning system that automatically generates structural layouts from building plan sketches, achieving an average error of 2.2% in column positions.
Structural engineering knowledge can be of significant importance to the architectural design team during the early design phase. However, architects and engineers do not typically work together during the conceptual phase; in fact, structural engineers are often called late into the process. As a result, updates in the design are more difficult and time-consuming to complete. At the same time, there is a lost opportunity for better design exploration guided by structural feedback. In general, the earlier in the design process the iteration happens, the greater the benefits in cost efficiency and informed de-sign exploration, which can lead to higher-quality creative results. In order to facilitate an informed exploration in the early design stage, we suggest the automation of fundamental structural engineering tasks and introduce ApproxiFramer, a Machine Learning-based system for the automatic generation of structural layouts from building plan sketches in real-time. The system aims to assist architects by presenting them with feasible structural solutions during the conceptual phase so that they proceed with their design with adequate knowledge of its structural implications. In this paper, we describe the system and evaluate the performance of a proof-of-concept implementation in the domain of orthogonal, metal, rigid structures. We trained a Convolutional Neural Net to iteratively generate structural design solutions for sketch-level building plans using a synthetic dataset and achieved an average error of 2.2% in the predicted positions of the columns.